Tidy data
Set-up
knitr::opts_chunk$set(
echo = TRUE,
error = FALSE,
message = FALSE,
warning = FALSE
)
library(afex)
library(tidyverse)
library(kableExtra)
library(knitr)
library(plotly)
library(broom)
library(wesanderson)
library(robust)
library(ggparl)
library(mediation)
set.seed(2022)Read data
# reads excel data sheet
data <- read.csv("../data/study4.csv") # reads data
data_sat <- read.csv("../data/raw/stage3_satisfaction.csv") # reads in satisfaction data
# summary(data) # explore the data types, missing data and typos
data <- data %>%
mutate(gender = ifelse(gender == "m", "m", "f")) # corrects an issuewith extra space in "f "
data$gender <- as.factor(data$gender)
data$pp_number <- as.factor(data$pp_number)
data$condition <-as.factor(data$condition)
# mood data
mood1 <- read.csv("../data/raw/stage1_mood.csv") %>%
dplyr::select(subject, stimulusitem2, response)%>%
rename(response1=response, feeling = stimulusitem2)%>%
mutate(stage = 1)%>%
filter(subject != 6666)
mood2 <- read.csv("../data/raw/stage2_mood.csv") %>%
dplyr::select(subject, stimulusitem2, response)%>%
rename(response2=response, feeling = stimulusitem2)%>%
mutate(stage = 2)%>%
filter(subject != 6666)
mood3 <- read.csv("../data/raw/stage3_mood.csv") %>%
dplyr::select(subject, stimulusitem2, response) %>%
rename(response3=response, feeling = stimulusitem2)%>%
mutate(stage = 3)%>%
filter(subject != 6666)
mood_dat <- inner_join( mood1, mood2,
by = c("subject", "feeling"),
keep = FALSE)%>%
inner_join(., mood3,
by = c("subject", "feeling"),
keep = FALSE)%>%
dplyr::select(- starts_with("stage")) %>%
pivot_longer(cols = response1:response3,
names_to ="stage",
values_to = "rating")%>%
pivot_wider(names_from = "feeling",
values_from ="rating" )%>%
mutate(alert = (alert +strong + `clear-headed` + `well-coordinated` + energetic + `quick-witted` + attentive + proficient + interested )/9,
content =(contented + tranquil + amicable + gregarious)/4,
calm =(calm + relaxed)/2)
#`clear-headed` + `well-coordinated` + energetic + `quick-witted` + attentive + proficient + interested
# qualtrics data
#mainly info about drinking
data_q<- read.csv("../data/other/study_4Q.csv")%>%
dplyr::select(Q21, gender, age, Q15, Q16)%>%
rename(subject = Q21,
units = Q15,
units_beer = Q16)%>%
mutate(subject = as.numeric(subject),
condition = subject %/% 1000)%>%
filter(!is.na(subject))%>%
mutate(subject = as.factor(subject),
gender=as.factor(gender),
age = as.numeric(age),
units = as.numeric(units),
units_beer = as.numeric(units_beer))%>%
filter( subject != "6666")
# expectations & perception of taste/flavour
taste_dat <- read.csv("../data/raw/stage1_perception.csv")%>%
dplyr::select(subject, trialcode, response)%>%
rename(Epercept = trialcode, expected = response)
taste_dat$Epercept<- as.factor(taste_dat$Epercept)
expect_dat<- read.csv("../data/raw/stage1_expectations.csv")%>%
dplyr::select(subject, trialcode, response, stimulusitem3)%>%
filter(stimulusitem3 == "extremely")%>%
dplyr::select(- stimulusitem3)%>%
rename(percept = trialcode, perceived = response)
expect_dat$percept<- as.factor(expect_dat$percept)
# satisfaction data
#merge data_sat and expect_dat
satis_datP <- data_sat %>%
dplyr::select(subject, trialcode, response)%>%
dplyr::filter(trialcode == "VAS")
satis_datE <- expect_dat %>%
dplyr::filter(percept == "VAS_satisE_VAS")
wtp_dat <- data_sat %>%
filter(trialcode == "VAS_wtp")%>%
dplyr::select(subject, response)
# ITT data
itt_s1 <-read.csv("../data/summaries/stage1_ITT.csv")%>%
dplyr::select(script.subjectid, expressions.propcorrect_stim1:expressions.propcorrect_stim13)%>%
mutate(stage1 = 1)%>%
rename(subject = script.subjectid)%>%
rename_at(vars(matches("^expressions")), ~ str_remove(.,"^expressions.propcorrect_")) %>%
pivot_longer(cols = starts_with("stim") ,
names_to = "stimulus" ,
values_to = "correct")
itt_s2 <-read.csv("../data/summaries/stage2_ITT.csv")%>%
dplyr::select(script.subjectid, expressions.propcorrect_stim1:expressions.propcorrect_stim13)%>%
mutate(stage2 = 2)%>%
rename(subject = script.subjectid)%>%
rename_at(vars(matches("^expressions")), ~ str_remove(.,"^expressions.propcorrect_"))%>%
pivot_longer(cols = starts_with("stim") ,
names_to = "stimulus" ,
values_to = "correct")
itt_s3 <-read.csv("../data/summaries/stage3_ITT.csv")%>%
dplyr::select(script.subjectid, expressions.propcorrect_stim1:expressions.propcorrect_stim13)%>%
mutate(stage3 = 3)%>%
rename(subject = script.subjectid)%>%
rename_at(vars(matches("^expressions")), ~ str_remove(.,"^expressions.propcorrect_"))%>%
pivot_longer(cols = starts_with("stim") ,
names_to = "stimulus" ,
values_to = "correct")
itt_dat <- inner_join(itt_s1, itt_s2,
by = c("subject","stimulus"),
keep = FALSE,
suffix = c(".1", ".2"))%>%
inner_join(., itt_s3,
by = c("subject", "stimulus"),
keep = FALSE) %>%
pivot_longer(cols = starts_with("correct") ,
names_to = "stage",
values_to = "correct")%>%
dplyr::select(-c( stage1, stage2, stage3))%>%
mutate( stage = case_when(stage == "correct.1" ~ 'stage 1',
stage == "correct.2" ~ "stage 2",
stage == "correct" ~ "stage 3"))%>%
mutate(condition = subject %/% 1000) %>%
mutate(alcohol = if_else((condition%%2)==1, "alcoholic", "non-alcoholic"))%>%
mutate(focus = case_when(condition == 1 ~ 'control',
condition == 2 ~ 'control',
condition == 3 ~ 'hedonic',
condition == 4 ~ 'hedonic',
condition == 5 ~ 'utilitarian',
condition == 6 ~ 'utilitarian'))Data joins
DA <- data %>%
dplyr::select(pp_number, BMI)%>%
rename(subject = pp_number)
DB <- data_sat %>%
dplyr::select(subject, response, trialcode ) %>%
mutate(row = row_number(), subject = as.factor(subject)) %>%
pivot_wider(names_from = trialcode, values_from = response) %>%
rename( wtp = VAS_wtp)%>%
dplyr::select(- row)
D1 <- inner_join( DA,DB,
by = "subject",
keep = FALSE
) %>%
pivot_longer(cols =c(VAS, wtp) ,
names_to = "measure",
values_to = "rating") %>%
inner_join(., data_q,
by = "subject",
keep = FALSE)%>%
filter(!is.na(rating))
# long dataset incl,. info about satisfaction and willingness to pay by condition, and alcohol/ non-alcohol
# need to add focus condition
#Joining mood data
D2 <- inner_join(mood1, mood2,
by = c("subject", "feeling"),
keep = FALSE) %>%
inner_join(., mood3,
by = c("subject","feeling"),
keep = FALSE)%>%
dplyr::select(- starts_with("stage")) %>%
mutate(condition = subject %/% 1000) %>%
pivot_longer(cols = starts_with("response"),
names_to = "stage",
names_prefix = "response",
values_to = "rating")
D3_dat <- inner_join(expect_dat, taste_dat,
by = "subject",
keep = FALSE)%>%
mutate(condition = subject %/% 1000) %>%
pivot_wider(names_from = percept,
values_from = perceived) %>%
pivot_wider(names_from = Epercept,
values_from = expected)%>%
rename_at(vars(matches("^VAS\\_")), ~ str_remove(.,"^VAS\\_"))%>%
rename_at(vars(matches("_VAS")), ~ str_remove(.,"_VAS")) %>%
mutate(alcohol = if_else((condition%%2)==1, "alcoholic", "non-alcoholic"))%>%
mutate(focus = case_when(condition == 1 ~ 'control',
condition == 2 ~ 'control',
condition == 3 ~ 'hedonic',
condition == 4 ~ 'hedonic',
condition == 5 ~ 'utilitarian',
condition == 6 ~ 'utilitarian'))
D3_dat$alcohol<- as.factor(D3_dat$alcohol)
D3_dat$focus<- as.factor(D3_dat$focus)
D3_dat$condition <- as.factor(D3_dat$condition)
satis_dat <- inner_join(satis_datE, satis_datP,
by = "subject",
keep = FALSE)%>%
dplyr::select(subject, perceived, response,)%>%
rename(satisE = perceived, satis = response)%>%
inner_join(., wtp_dat,
by = "subject",
keep = FALSE)%>%
rename(wtp = response) %>%
mutate(condition = subject %/% 1000) %>%
mutate(alcohol = if_else((condition%%2)==1, "alcoholic", "non-alcoholic"))%>%
mutate(focus = case_when(condition == 1 ~ 'control',
condition == 2 ~ 'control',
condition == 3 ~ 'hedonic',
condition == 4 ~ 'hedonic',
condition == 5 ~ 'utilitarian',
condition == 6 ~ 'utilitarian'))Descriptive analyses
Differences between conditions
# gender
total_n <- nrow(data)
males <- data_q%>%filter(gender == "male") %>% nrow()
females <- data_q%>%filter(gender == "female") %>% nrow()
nonbinary <- data_q %>% filter(gender== "non-binary" )%>% nrow()
# by condition
gender_tib <- D1 %>%
filter(measure == "wtp") %>% # avoids duplicating data
group_by( condition) %>%
summarise(n=n(),
BMI = mean(BMI, na.rm = TRUE),
age = mean(age, na.rm = TRUE)) %>%
kable()Gender
xtabs(~ gender + condition , data = data_q,drop.unused.levels = TRUE)%>%
kable(digits = 2, caption = "A contingency table with observed gender values/counts across conditions") %>%
kable_styling(full_width = TRUE,
position = "left")| 1 | 2 | 3 | 4 | 5 | 6 | |
|---|---|---|---|---|---|---|
| female | 24 | 21 | 22 | 25 | 30 | 23 |
| male | 6 | 8 | 7 | 6 | 6 | 8 |
| non-binary | 2 | 1 | 1 | 1 | 0 | 0 |
chi_test <- chisq.test(data_q$gender, data_q$condition, simulate.p.value = TRUE)
chi_test$expected %>%
kable(digits = 1, caption = "A contingency table of expected gender values/counts across conditions")%>%
kable_styling(full_width = TRUE,
position = "left")| 1 | 2 | 3 | 4 | 5 | 6 | |
|---|---|---|---|---|---|---|
| female | 24.3 | 22.8 | 22.8 | 24.3 | 27.3 | 23.5 |
| male | 6.9 | 6.4 | 6.4 | 6.9 | 7.7 | 6.7 |
| non-binary | 0.8 | 0.8 | 0.8 | 0.8 | 0.9 | 0.8 |
# χ2 test requires all expected frequencies to be greater than five --> simulate.p.value avoids the problemAltogether 189 participants took part in the study, as is common for psychology studies, most of these were women (145 self-identified as female, 41 as male and 5 as non-binary). The gender split across the conditions was not problematic \(\chi^2\) = 5.28, p = 0.903 (simulated p values based on 2000 replicates, as expected values << 5).
# plot
## bar plot is best here
gender_pal <- c("#d62828", "#003049", "#9d69a3")
gender_cond_plot <-
ggplot(data = data_q)+
geom_bar(aes(x= condition, fill = gender))+
scale_fill_manual(values = gender_pal) +
scale_y_continuous(name="", limits=c(0, 40), breaks = seq(0,40, 5)) +
scale_x_discrete(name = "conditions", breaks = seq(1,6,1), limits = c("1", "2", "3", "4", "5", "6"))+
theme(panel.grid = element_blank())+
theme_minimal()
ggplotly(gender_cond_plot) Number of participants in each conditions
Age
data_desc <- D1 %>%
filter(measure == "wtp")
age_lm <- lm(age ~ condition, data=data_desc)
age_sum <- broom::tidy(age_lm)
age_glance <- broom::glance(age_lm)Participants’ age ranged between 18 and 57. Most participants were very young and the mean age of participants was 20.3 (sd = 4.7). The age did not significantly differ across the six conditions F(181, 1) = 0.26, p = 0.608.
age_plot <- ggplot(data= data_desc, aes(x=condition, y = age, group = condition) )+
geom_boxplot(fill= "#6c757d", alpha = 0.5, outlier.shape = NA) +
geom_jitter(shape=16, position=position_jitter(0.2), aes(color = gender))+
scale_y_continuous(name="age", limits=c(18, 60), breaks = seq(0,60, 10)) +
scale_x_discrete(name = "conditions", limits = c("1", "2", "3", "4", "5", "6"))+
scale_color_manual(values = gender_pal) +
theme_minimal()
age_plot2 <- ggplot(data= data_desc, aes(x=condition, y = age, group = condition) )+
geom_boxplot(fill= "#6c757d", alpha = 0.5, outlier.shape = NA) +
geom_jitter(shape=16, position=position_jitter(0.2), aes(color = gender))+
scale_y_continuous(name="age", limits=c(15, 30), breaks = seq(15,30,5)) +
scale_x_discrete(name = "conditions", limits = c("1", "2", "3", "4", "5", "6"))+
scale_color_manual(values = gender_pal) +
theme_minimal()
ggplotly(age_plot)Participants’ age by condition and gender. Note range of the y-axis
ggplotly(age_plot2)Participants’ age by condition and gender. Note range of the y-axis
BMI
bmi_lm <- lm(BMI ~ condition, data=data_desc)
bmi_glance <- broom::glance(bmi_lm)The participants’ BMI ranged between 16.7 - 36.1. The average BMI was 23.1 (sd =3.7) and again, this did not differ across conditions F(186, 1) = 1.62, p = 0.205.
BMI_plot <- ggplot(aes(x=condition, y = BMI), data= data_desc)+
geom_boxplot(fill= "#6c757d", alpha = 0.5) +
geom_jitter(shape=16, position=position_jitter(0.2), aes(color = gender))+
scale_y_continuous(name="BMI", limits=c(18, 35), breaks = seq(0,35, 10)) +
scale_x_discrete(name = "conditions", limits = c("1", "2", "3", "4", "5", "6"))+
scale_color_manual(values = gender_pal) +
theme_minimal()
ggplotly(BMI_plot)Participants’ BMI by condition and gender
Baseline mood
Visual inspection of participants’ baseline mood does not give raise to any concerns. Do I need to run 16 anovas? Does not seem necessary…)
mood_base <- D2 %>%
filter(stage == 1)
palette <- c("#033f63","#28666e","#7c9885","#b5b682","#fedc97","#e5e1ee","#c17767","#210203","#eb9486","#82204a", "#0a2342","#2ca58d","#84bc9c","#fffdf7","#f46197","#fec601")
ggplot(data = mood_base, aes(x=condition, y = rating, group = condition, fill = feeling))+
geom_boxplot()+
facet_wrap("feeling")+
scale_fill_manual(values = palette) +
scale_y_continuous(name="", limits=c(0, 100), breaks = seq(0,100, 20)) +
theme(panel.grid = element_blank())+
theme_minimal()
Participants’ baseline mood ratings
Drinking
alcohol consumption
While the average alcohol consumption was realtively low 10.6 (sd = 8.9), there was a considerable differences among participants. The lowest alcohol consumption was only 1, equivalent to a half a pint of light beer (3.5%) once a week, the highest alcohol consumption was 70 units of alcohol a week, which is equivalent to over 20 pints of strong (~5% abv) beer every week. Interestingly, 45 (23.8%) participants consumed more than the recommended 14 units a week. We also asked participants to estimate how many units of beer they consumed. This was on average 4.8 (sd = 4.7), this however varied between 0 and 30. Thefigures below show participants’ weekly alcohol consumption, weekly beer consumption and the proportion of units of alcohol that came from beer. Neither the overall alcohol consumption nor beer consumption differed significantly across the conditions, F(2, 189) = 3.19, p = 0.069 and F(2, 189) = 0.06, p = 0.804, respectively.
# plot units alcohol consumption by condition
units_plot<-ggplot(aes(x=condition,
y = units,
group = condition),
data= data_q)+
geom_hline(aes(yintercept = 14, linetype = "14 units/week"),
color = "red")+
scale_linetype_manual(name = "max. recommended\nalcohol consumption", values = c(2, 2),
guide = guide_legend(override.aes = list(color = c("red")), title.theme = element_text(size = 8, face = "bold" ) ))+
geom_boxplot(fill= "#6c757d",
alpha = 0.5,
outlier.shape = NA) +
geom_jitter(shape=16,
position=position_jitter(0.2),
aes(color = gender))+
scale_y_continuous(name="alcohol consumption\n(units)",
limits=c(0, 75),
breaks = seq(0,75, 5)) +
scale_x_discrete(name = "conditions",
limits = c("1", "2", "3", "4", "5", "6"))+
scale_color_manual(values = gender_pal) +
theme_minimal()
ggplotly(units_plot)participants’ alcohol consumption by condition
# plot units beer consumption by condition
beer_plot<-ggplot(aes(x=condition,
y = units_beer,
group = condition),
data= data_q)+
geom_boxplot(fill= "#6c757d",
alpha = 0.5,
outlier.shape = NA) +
geom_jitter(shape=16,
position=position_jitter(0.2),
aes(color = gender))+
scale_y_continuous(name="beer consumption\n(units)",
limits=c(0, 35),
breaks = seq(0,35, 5)) +
scale_x_discrete(name = "conditions",
limits = c("1", "2", "3", "4", "5", "6"))+
scale_color_manual(values = gender_pal) +
theme_minimal()
ggplotly(beer_plot)participants’ beer consumption by condition
summary_beer <- data_q %>%
pivot_longer(cols = starts_with("units"), names_to = "al_id", values_to = "units") %>%
mutate(al_id = ifelse(al_id =="units", "all", "beer"))%>%
group_by(condition, al_id)%>%
summarize(units = mean(units))%>%
pivot_wider(names_from = al_id, values_from = units)%>%
mutate(perc= (beer/all)*100)%>%
pivot_longer(cols = c(all, beer), names_to = "alcohol", values_to = "units")
al_pal <- c( "#1d3557","#f4a261")
beer_prop <- ggplot(data=summary_beer,
aes(x=condition,
y=units,
fill = alcohol)) +
geom_bar(stat="identity",
position=position_dodge())+
geom_text(aes(label =paste0(round(perc,1),"%") ),
data = summary_beer %>%filter(alcohol == "beer"),
show.legend = FALSE,
size = 2,
nudge_x = 0.3,
nudge_y = 1,
color = "#1d3557" )+
scale_fill_manual(values = al_pal)+
scale_y_continuous(name="alcohol consumption\n(units)",
limits=c(0, 15),
breaks = seq(0,15, 5)) +
scale_x_discrete(name = "conditions",
limits = c("1", "2", "3", "4", "5", "6"))+
labs( fill = "alcohol")+
theme_minimal()
ggplotly(beer_prop)% of alcohol consumption that is beer by condition
Expectations & Gustatory Perception
- structural equation modelling
- gustatory data (expected and perceived are in )
- because of independent measures design we can use the
mediationpackage, functionmediate()some resources and documentation
Bitterness
- no direct or indirect effect of focus or alcoholcontent on perceived orexpected bitterness
bitter_dat <- D3_dat%>%
dplyr::select(subject, alcohol, focus, condition, bitterE, bitter)
# prep dataset
# model m A --> M
mbm <- lm(bitterE ~ focus*alcohol, data = bitter_dat)
# model A--> B
mby <-lm(bitter ~ bitterE + focus*alcohol, data = bitter_dat)
# robustSE = TRUE to get heteroscedasticity consistent SE
# nonparametric bootstrap rather than the quasi-Bayesian Monte
# Carlo simulation for variance estimation via the boot = TRUE argument
# also entering alcohol/focus as a covariate
broom::tidy(mbm)## # A tibble: 6 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 55.5 2.80 19.8 1.61e-47
## 2 focushedonic 5.26 4.06 1.29 1.97e- 1
## 3 focusutilitarian -3.08 3.85 -0.801 4.24e- 1
## 4 alcoholnon-alcoholic -3.70 4.03 -0.919 3.59e- 1
## 5 focushedonic:alcoholnon-alcoholic 1.22 5.72 0.214 8.31e- 1
## 6 focusutilitarian:alcoholnon-alcoholic 6.99 5.59 1.25 2.13e- 1med_bitter_focusH <- mediate(mbm, mby,
sims = 1000,
boot = TRUE,
boot.ci.type = "perc",
treat = "focus",
mediator = "bitterE",
covariates = "alcohol",
robustSE = TRUE,
conf.level = 0.95,
control.value = "control",
treat.value = "hedonic",)
med_bitter_focusU <- mediate(mbm, mby,
sims = 1000,
boot = TRUE,
boot.ci.type = "perc",
treat = "focus",
mediator = "bitterE",
covariates = "alcohol",
robustSE = TRUE,
conf.level = 0.95,
control.value = "control",
treat.value = "utilitarian")
summary(med_bitter_focusH)##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## (Inference Conditional on the Covariate Values Specified in `covariates')
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 1.5961 0.0275 3.82 0.044 *
## ADE 1.6530 -5.8790 9.24 0.678
## Total Effect 3.2490 -4.2910 10.74 0.422
## Prop. Mediated 0.4912 -3.3536 3.22 0.446
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 190
##
##
## Simulations: 1000plot(med_bitter_focusH)
summary(med_bitter_focusU)##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## (Inference Conditional on the Covariate Values Specified in `covariates')
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.0925 -1.5001 1.73 0.88
## ADE 3.7717 -3.0102 10.89 0.32
## Total Effect 3.8642 -3.2772 10.99 0.29
## Prop. Mediated 0.0239 -1.5064 1.58 0.86
##
## Sample Size Used: 190
##
##
## Simulations: 1000plot(med_bitter_focusU)
med_bitter_alcohol <- mediate(mbm, mby,
sims = 1000,
boot.ci.type = "perc",
treat = "alcohol",
mediator = "bitterE",
robustSE = TRUE,
boot = TRUE,
conf.level = 0.95,
covariates = "focus",
control.value = "alcoholic",
treat.value = "non-alcoholic")
summary(med_bitter_alcohol)##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## (Inference Conditional on the Covariate Values Specified in `covariates')
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME -0.229 -1.629 1.14 0.70
## ADE -0.167 -6.500 6.16 0.99
## Total Effect -0.397 -6.912 5.91 0.92
## Prop. Mediated 0.578 -1.912 2.72 0.87
##
## Sample Size Used: 190
##
##
## Simulations: 1000plot(med_bitter_alcohol)
bitter_tib <-bitter_dat %>%
pivot_longer(cols = starts_with("bitter"),
names_to = "bitter",
values_to = "rating")%>%
group_by(alcohol, focus,bitter)%>%
summarise(n = n(),
meanB = mean(rating, na.rm = TRUE),
sdB = sd(rating, na.rm = TRUE),
ci_lo = meanB - 1.96 * sdB/sqrt(n),
ci_hi = meanB + 1.96 * sdB/sqrt(n))%>%
filter(!is.na(focus))
rating_labs <- c("perceived", "expected")
names(rating_labs) <- c("bitter", "bitterE")
bitter_plot<-ggplot(aes(x= focus, y = meanB, fill = alcohol), data = bitter_tib ) +
facet_grid(~ bitter,
labeller = labeller(bitter = rating_labs))+
geom_bar(stat="identity", position=position_dodge())+
geom_errorbar(aes(ymin=ci_lo, ymax=ci_hi), width=.2,
position=position_dodge(.9))+
scale_y_continuous(name="bitterness",
limits=c(0, 100),
breaks = seq(0,100, 25)) +
scale_x_discrete(name = "focus" )+
scale_fill_manual(values = gender_pal) +
theme_minimal()
ggplotly(bitter_plot)Expected and perceived ratings of bitterness errorbars indicate 95% CI (1.96 SE)
Refreshment
- no direct or indirect effect of focus/ alcohol content on perceived refreshment
- focus on effects of drink –> increased expectations of refreshment, but did not affect perception
refresh_dat <- D3_dat%>%
dplyr::select(subject, alcohol, focus, condition, refreshmentE, refreshment)
# prep dataset
# model m A --> M
mrm <- lm(refreshmentE ~ focus*alcohol, data = refresh_dat)
# model A--> B
mry <-lm(refreshment ~ refreshmentE + focus*alcohol, data = refresh_dat)
broom::tidy(mrm)%>%
kable(digits = 3, caption = "effect of alcohol and focus on expected refreshment")%>%
kable_styling(full_width = TRUE)| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 63.250 | 3.052 | 20.725 | 0.000 |
| focushedonic | 0.474 | 4.426 | 0.107 | 0.915 |
| focusutilitarian | 6.472 | 4.194 | 1.543 | 0.125 |
| alcoholnon-alcoholic | -2.417 | 4.387 | -0.551 | 0.582 |
| focushedonic:alcoholnon-alcoholic | 2.818 | 6.232 | 0.452 | 0.652 |
| focusutilitarian:alcoholnon-alcoholic | -0.435 | 6.095 | -0.071 | 0.943 |
med_refreshment_focusH <- mediate(mrm, mry,
sims = 1000,
boot = TRUE,
boot.ci.type = "perc",
treat = "focus",
mediator = "refreshmentE",
covariates = "alcohol",
robustSE = TRUE,
conf.level = 0.95,
control.value = "control",
treat.value = "hedonic")
summary(med_refreshment_focusH)##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## (Inference Conditional on the Covariate Values Specified in `covariates')
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.476 -1.180 1.99 0.518
## ADE -5.184 -10.226 0.31 0.064 .
## Total Effect -4.708 -9.785 0.80 0.090 .
## Prop. Mediated -0.101 -1.410 0.73 0.584
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 190
##
##
## Simulations: 1000plot(med_refreshment_focusH)
med_refreshment_focusU <- mediate(mrm, mry,
sims = 1000,
boot = TRUE,
boot.ci.type = "perc",
treat = "focus",
mediator = "refreshmentE",
covariates = "alcohol",
robustSE = TRUE,
conf.level = 0.95,
control.value = "control",
treat.value = "utilitarian")
summary(med_refreshment_focusU)##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## (Inference Conditional on the Covariate Values Specified in `covariates')
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 1.6072 0.0126 3.59 0.046 *
## ADE -3.8968 -8.8457 1.17 0.120
## Total Effect -2.2895 -7.5390 3.10 0.418
## Prop. Mediated -0.7020 -11.5112 7.92 0.456
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 190
##
##
## Simulations: 1000plot(med_refreshment_focusU)
med_refreshment_alcohol <- mediate(mrm, mry,
sims = 1000,
boot.ci.type = "perc",
treat = "alcohol",
mediator = "refreshmentE",
robustSE = TRUE,
boot = TRUE,
conf.level = 0.95,
covariates = "focus",
control.value = "alcoholic",
treat.value = "non-alcoholic")
summary(med_refreshment_alcohol)##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## (Inference Conditional on the Covariate Values Specified in `covariates')
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME -0.428 -1.945 0.80 0.50
## ADE -1.600 -5.955 2.61 0.43
## Total Effect -2.028 -6.515 2.32 0.33
## Prop. Mediated 0.211 -2.628 2.81 0.56
##
## Sample Size Used: 190
##
##
## Simulations: 1000plot(med_refreshment_alcohol)
refreshment_tib <-refresh_dat %>%
pivot_longer(cols = starts_with("refreshment"),
names_to = "refreshment",
values_to = "rating")%>%
group_by(alcohol, focus,refreshment)%>%
summarise(n = n(),
meanB = mean(rating, na.rm = TRUE),
sdB = sd(rating, na.rm = TRUE),
ci_lo = meanB - 1.96 * sdB/sqrt(n),
ci_hi = meanB + 1.96 * sdB/sqrt(n))%>%
filter(!is.na(focus))
rating_labs <- c("perceived", "expected")
names(rating_labs) <- c("refreshment", "refreshmentE")
refreshment_plot <- ggplot(aes(x= focus, y = meanB, fill = alcohol), data = refreshment_tib ) +
facet_grid(~ refreshment,
labeller = labeller(refreshment = rating_labs))+
geom_bar(stat="identity", position=position_dodge())+
geom_errorbar(aes(ymin=ci_lo, ymax=ci_hi), width=.2,
position=position_dodge(.9))+
scale_y_continuous(name="refreshment",
limits=c(0, 100),
breaks = seq(0,100, 25)) +
scale_x_discrete(name = "focus" )+
scale_fill_manual(values = gender_pal) +
theme_minimal()
ggplotly(refreshment_plot)Expected and perceived ratings of Refreshment errorbars indicate 95% CI (1.96 SE)
Liking
- no effect of focus or alcohol content on expected or perceived liking
liking_dat <- D3_dat%>%
dplyr::select(subject, alcohol, focus, condition, likingE, liking)
# prep dataset
# model m A --> M
mlm <- lm(likingE ~ focus*alcohol, data = liking_dat)
# model A--> B
mly <-lm(liking ~ likingE + focus*alcohol, data = liking_dat)
broom::tidy(mlm)%>%
kable(digits = 3, caption = "effect of alcohol and focus on expected liking")%>%
kable_styling(full_width = TRUE)| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 58.594 | 3.404 | 17.212 | 0.000 |
| focushedonic | -6.249 | 4.937 | -1.266 | 0.207 |
| focusutilitarian | -1.455 | 4.679 | -0.311 | 0.756 |
| alcoholnon-alcoholic | -5.394 | 4.894 | -1.102 | 0.272 |
| focushedonic:alcoholnon-alcoholic | 2.955 | 6.952 | 0.425 | 0.671 |
| focusutilitarian:alcoholnon-alcoholic | -0.261 | 6.798 | -0.038 | 0.969 |
med_liking_focusH <- mediate(mlm, mly,
sims = 1000,
boot = TRUE,
boot.ci.type = "perc",
treat = "focus",
mediator = "likingE",
covariates = "alcohol",
robustSE = TRUE,
conf.level = 0.95,
control.value = "control",
treat.value = "hedonic")
summary(med_liking_focusH)##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## (Inference Conditional on the Covariate Values Specified in `covariates')
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME -0.745 -2.151 0.39 0.202
## ADE -5.091 -11.422 1.42 0.112
## Total Effect -5.835 -12.054 0.77 0.078 .
## Prop. Mediated 0.128 -0.465 0.89 0.256
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 190
##
##
## Simulations: 1000plot(med_liking_focusH)
med_liking_focusU <- mediate(mlm, mly,
sims = 1000,
boot = TRUE,
boot.ci.type = "perc",
treat = "focus",
mediator = "likingE",
covariates = "alcohol",
robustSE = TRUE,
conf.level = 0.95,
control.value = "control",
treat.value = "utilitarian")
summary(med_liking_focusU)##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## (Inference Conditional on the Covariate Values Specified in `covariates')
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME -0.2454 -1.3588 0.92 0.67
## ADE -3.0973 -9.3443 2.94 0.30
## Total Effect -3.3427 -9.8839 2.99 0.27
## Prop. Mediated 0.0734 -1.3463 1.02 0.65
##
## Sample Size Used: 190
##
##
## Simulations: 1000plot(med_liking_focusU)
med_liking_alcohol <- mediate(mlm, mly,
sims = 1000,
boot.ci.type = "perc",
treat = "alcohol",
mediator = "likingE",
robustSE = TRUE,
boot = TRUE,
conf.level = 0.95,
covariates = "focus",
control.value = "alcoholic",
treat.value = "non-alcoholic")
summary(med_liking_alcohol)##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## (Inference Conditional on the Covariate Values Specified in `covariates')
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME -0.703 -2.170 0.11 0.12
## ADE -3.393 -8.464 2.01 0.20
## Total Effect -4.096 -9.395 1.24 0.11
## Prop. Mediated 0.172 -0.529 1.51 0.20
##
## Sample Size Used: 190
##
##
## Simulations: 1000plot(med_liking_alcohol)
liking_tib <-liking_dat %>%
pivot_longer(cols = starts_with("liking"),
names_to = "liking",
values_to = "rating")%>%
group_by(alcohol, focus,liking)%>%
summarise(n = n(),
meanB = mean(rating, na.rm = TRUE),
sdB = sd(rating, na.rm = TRUE),
ci_lo = meanB - 1.96 * sdB/sqrt(n),
ci_hi = meanB + 1.96 * sdB/sqrt(n))%>%
filter(!is.na(focus))
rating_labs <- c("perceived", "expected")
names(rating_labs) <- c("liking", "likingE")
liking_plot <- ggplot(aes(x= focus, y = meanB, fill = alcohol), data = liking_tib ) +
facet_grid(~ liking,
labeller = labeller(liking = rating_labs))+
geom_bar(stat="identity", position=position_dodge())+
geom_errorbar(aes(ymin=ci_lo, ymax=ci_hi), width=.2,
position=position_dodge(.9))+
scale_y_continuous(name="liking",
limits=c(0, 100),
breaks = seq(0,100, 25)) +
scale_x_discrete(name = "focus" )+
scale_fill_manual(values = gender_pal) +
theme_minimal()
ggplotly(liking_plot)Expected and perceived ratings of liking errorbars indicate 95% CI (1.96 SE)
Body
- alcoholic beer rated as having fuller body vs. non-alcoholic beer, however not mediated by expectations of body -no effect of focus on expected or perceived body
body_dat <- D3_dat%>%
dplyr::select(subject, alcohol, focus, condition, bodyE, body)
# prep dataset
# model m A --> M
mbom <- lm(bodyE ~ focus*alcohol, data = body_dat)
# model A--> B
mboy <-lm(body ~ bodyE + focus*alcohol, data = body_dat)
broom::tidy(mbom)%>%
kable(digits = 3, caption = "effect of alcohol and focus on expected body") %>%
kable_styling(full_width = TRUE)| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 48.594 | 3.100 | 15.676 | 0.000 |
| focushedonic | 1.682 | 4.496 | 0.374 | 0.709 |
| focusutilitarian | 3.240 | 4.260 | 0.760 | 0.448 |
| alcoholnon-alcoholic | -7.727 | 4.456 | -1.734 | 0.085 |
| focushedonic:alcoholnon-alcoholic | 6.857 | 6.330 | 1.083 | 0.280 |
| focusutilitarian:alcoholnon-alcoholic | 3.636 | 6.190 | 0.587 | 0.558 |
med_body_focusH <- mediate(mbom, mboy,
sims = 1000,
boot = TRUE,
boot.ci.type = "perc",
treat = "focus",
mediator = "bodyE",
covariates = "alcohol",
robustSE = TRUE,
conf.level = 0.95,
control.value = "control",
treat.value = "hedonic")
summary(med_body_focusH)##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## (Inference Conditional on the Covariate Values Specified in `covariates')
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.996 -0.152 3.36 0.14
## ADE 1.278 -5.479 7.96 0.67
## Total Effect 2.273 -4.116 9.01 0.48
## Prop. Mediated 0.438 -2.955 2.96 0.53
##
## Sample Size Used: 190
##
##
## Simulations: 1000plot(med_body_focusH)
med_body_focusU <- mediate(mbom, mboy,
sims = 1000,
boot = TRUE,
boot.ci.type = "perc",
treat = "focus",
mediator = "bodyE",
covariates = "alcohol",
robustSE = TRUE,
conf.level = 0.95,
control.value = "control",
treat.value = "utilitarian")
summary(med_body_focusU)##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## (Inference Conditional on the Covariate Values Specified in `covariates')
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.9918 -0.0983 3.05 0.10
## ADE -1.6508 -8.3809 5.66 0.64
## Total Effect -0.6590 -7.5640 6.56 0.86
## Prop. Mediated -1.5049 -4.2345 4.24 0.91
##
## Sample Size Used: 190
##
##
## Simulations: 1000plot(med_body_focusU)
med_body_alcohol <- mediate(mbom, mboy,
sims = 1000,
boot.ci.type = "perc",
treat = "alcohol",
mediator = "bodyE",
robustSE = TRUE,
boot = TRUE,
conf.level = 0.95,
covariates = "focus",
control.value = "alcoholic",
treat.value = "non-alcoholic")
summary(med_body_alcohol)##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## (Inference Conditional on the Covariate Values Specified in `covariates')
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME -0.8385 -2.5845 0.19 0.148
## ADE -6.1981 -12.1954 -0.50 0.042 *
## Total Effect -7.0366 -13.0981 -1.26 0.016 *
## Prop. Mediated 0.1192 -0.0543 0.58 0.156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 190
##
##
## Simulations: 1000plot(med_body_alcohol)
body_tib <-body_dat %>%
pivot_longer(cols = starts_with("body"),
names_to = "body",
values_to = "rating")%>%
group_by(alcohol, focus,body)%>%
summarise(n = n(),
meanB = mean(rating, na.rm = TRUE),
sdB = sd(rating, na.rm = TRUE),
ci_lo = meanB - 1.96 * sdB/sqrt(n),
ci_hi = meanB + 1.96 * sdB/sqrt(n))%>%
filter(!is.na(focus))
rating_labs <- c("perceived", "expected")
names(rating_labs) <- c("body", "bodyE")
body_plot <-ggplot(aes(x= focus, y = meanB, fill = alcohol), data = body_tib ) +
facet_grid(~ body,
labeller = labeller(body = rating_labs))+
geom_bar(stat="identity", position=position_dodge())+
geom_errorbar(aes(ymin=ci_lo, ymax=ci_hi), width=.2,
position=position_dodge(.9))+
scale_y_continuous(name="body",
limits=c(0, 100),
breaks = seq(0,100, 25)) +
scale_x_discrete(name = "focus" )+
scale_fill_manual(values = gender_pal) +
theme_minimal()
ggplotly(body_plot)Expected and perceived ratings of body errorbars indicate 95% CI (1.96 SE)
Post - ingestive experience
Satisfaction
- no effect of focus and alcohol content on expected satisfaction
- participants rated perceived satisfaction lower when they drank non-alcoholic beer
- effect of alcohol on perceived satisfaction not mediated by expected satisfaction
satis_tib <-satis_dat %>%
pivot_longer(cols = starts_with("satis"),
names_to = "satis",
values_to = "rating")%>%
group_by(alcohol, focus,satis)%>%
summarise(n = n(),
meanB = mean(rating, na.rm = TRUE),
sdB = sd(rating, na.rm = TRUE),
ci_lo = meanB - 1.96 * sdB/sqrt(n),
ci_hi = meanB + 1.96 * sdB/sqrt(n))%>%
filter(!is.na(focus))
rating_labs <- c("perceived", "expected")
names(rating_labs) <- c("satis", "satisE")
satis_plot<-ggplot(aes(x= focus, y = meanB, fill = alcohol), data = satis_tib ) +
facet_grid(~ satis,
labeller = labeller(satis = rating_labs))+
geom_bar(stat="identity", position=position_dodge())+
geom_errorbar(aes(ymin=ci_lo, ymax=ci_hi), width=.2,
position=position_dodge(.9))+
scale_y_continuous(name="satisfaction",
limits=c(0, 100),
breaks = seq(0,100, 25)) +
scale_x_discrete(name = "focus" )+
scale_fill_manual(values = gender_pal) +
theme_minimal()
ggplotly(satis_plot)Expected and perceived ratings of satisfaction errorbars indicate 95% CI (+/- 1.96 SE)
# model m A --> M
msm <- lm(satisE ~ focus*alcohol, data = satis_dat)
# model A--> B
msy <-lm(satis ~ satisE + focus*alcohol, data = satis_dat)
broom::tidy(msm)%>%
kable(digits = 3, caption = "effect of alcoholand focus on expected satisfaction")%>%
kable_styling(full_width = TRUE)| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 55.500 | 3.088 | 17.972 | 0.000 |
| focushedonic | -1.893 | 4.444 | -0.426 | 0.671 |
| focusutilitarian | -0.203 | 4.155 | -0.049 | 0.961 |
| alcoholnon-alcoholic | -8.500 | 4.367 | -1.946 | 0.053 |
| focushedonic:alcoholnon-alcoholic | 5.955 | 6.183 | 0.963 | 0.337 |
| focusutilitarian:alcoholnon-alcoholic | 7.803 | 6.028 | 1.294 | 0.197 |
med_satis_focusU <- mediate(msm, msy,
sims = 1000,
boot = TRUE,
boot.ci.type = "perc",
treat = "focus",
mediator = "satisE",
covariates = "alcohol",
robustSE = TRUE,
conf.level = 0.95,
control.value = "control",
treat.value = "utilitarian")
summary(med_satis_focusU)##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## (Inference Conditional on the Covariate Values Specified in `covariates')
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.682 -0.338 2.20 0.22
## ADE -0.476 -6.678 5.83 0.88
## Total Effect 0.206 -6.527 6.45 0.94
## Prop. Mediated 3.312 -3.778 4.27 0.88
##
## Sample Size Used: 187
##
##
## Simulations: 1000plot(med_satis_focusU)
med_satis_focusH <- mediate(msm, msy,
sims = 1000,
boot = TRUE,
boot.ci.type = "perc",
treat = "focus",
mediator = "satisE",
covariates = "alcohol",
robustSE = TRUE,
conf.level = 0.95,
control.value = "control",
treat.value = "hedonic")
summary(med_satis_focusH)##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## (Inference Conditional on the Covariate Values Specified in `covariates')
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.194 -1.087 1.61 0.78
## ADE -4.819 -12.295 2.51 0.22
## Total Effect -4.625 -12.059 2.96 0.26
## Prop. Mediated -0.042 -1.181 1.09 0.90
##
## Sample Size Used: 187
##
##
## Simulations: 1000plot(med_satis_focusH)
med_satis_alcohol <- mediate(msm, msy,
sims = 1000,
boot.ci.type = "perc",
treat = "alcohol",
mediator = "satisE",
robustSE = TRUE,
boot = TRUE,
conf.level = 0.95,
covariates = "focus",
control.value = "alcoholic",
treat.value = "non-alcoholic")
summary(med_satis_alcohol)##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## (Inference Conditional on the Covariate Values Specified in `covariates')
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME -0.7113 -2.1322 0.17 0.150
## ADE -7.0777 -12.2471 -1.40 0.012 *
## Total Effect -7.7890 -13.0529 -2.07 0.008 **
## Prop. Mediated 0.0913 -0.0279 0.40 0.154
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 187
##
##
## Simulations: 1000plot(med_satis_alcohol)
Willingness to pay
- no effect of focus on willingness to pay
- effect of alcoholcontent on wtp ( pp would pay slightly more for alcoholic beer)
# model m A --> M
mwm <- lm(satis ~ focus*alcohol, data = satis_dat)
# model A--> B
mwy <-lm(wtp ~ satis + focus*alcohol, data = satis_dat)
med_wtp_focusH <- mediate(mwm, mwy,
sims = 1000,
boot = TRUE,
boot.ci.type = "perc",
treat = "focus",
mediator = "satis",
covariates = "alcohol",
robustSE = TRUE,
conf.level = 0.95,
control.value = "control",
treat.value = "hedonic")
summary(med_wtp_focusH)##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## (Inference Conditional on the Covariate Values Specified in `covariates')
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME -0.0943 -0.2411 0.05 0.21
## ADE -0.0658 -0.3619 0.20 0.63
## Total Effect -0.1601 -0.4736 0.15 0.31
## Prop. Mediated 0.5889 -3.6505 4.28 0.36
##
## Sample Size Used: 187
##
##
## Simulations: 1000plot(med_wtp_focusH)
med_wtp_focusU <- mediate(mwm, mwy,
sims = 1000,
boot = TRUE,
boot.ci.type = "perc",
treat = "focus",
mediator = "satis",
covariates = "alcohol",
robustSE = TRUE,
conf.level = 0.95,
control.value = "control",
treat.value = "utilitarian")
summary(med_wtp_focusU)##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## (Inference Conditional on the Covariate Values Specified in `covariates')
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.0042 -0.1337 0.14 0.95
## ADE -0.1707 -0.4372 0.10 0.20
## Total Effect -0.1665 -0.4779 0.15 0.28
## Prop. Mediated -0.0252 -3.1410 3.37 0.85
##
## Sample Size Used: 187
##
##
## Simulations: 1000plot(med_wtp_focusU)
med_wtp_alcohol <- mediate(mwm, mwy,
sims = 1000,
boot.ci.type = "perc",
treat = "alcohol",
mediator = "satis",
robustSE = TRUE,
boot = TRUE,
conf.level = 0.95,
covariates = "focus",
control.value = "alcoholic",
treat.value = "non-alcoholic")
summary(med_wtp_alcohol)##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## (Inference Conditional on the Covariate Values Specified in `covariates')
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME -0.1588 -0.2755 -0.04 0.004 **
## ADE -0.0949 -0.3235 0.13 0.402
## Total Effect -0.2536 -0.5000 -0.01 0.036 *
## Prop. Mediated 0.6260 0.1091 2.85 0.036 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 187
##
##
## Simulations: 1000plot(med_wtp_alcohol)
wtp_model <- robust::lmRob(wtp ~ focus * alcohol, data = satis_dat)
summary(wtp_model)##
## Call:
## robust::lmRob(formula = wtp ~ focus * alcohol, data = satis_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1655 -0.7333 0.1019 0.8345 2.1912
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.1655 0.1790 17.683 <2e-16 ***
## focushedonic -0.2674 0.2554 -1.047 0.2966
## focusutilitarian -0.3567 0.2403 -1.484 0.1394
## alcoholnon-alcoholic -0.4321 0.2510 -1.721 0.0869 .
## focushedonic:alcoholnon-alcoholic 0.1903 0.3539 0.538 0.5915
## focusutilitarian:alcoholnon-alcoholic 0.3233 0.3474 0.931 0.3532
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9126 on 181 degrees of freedom
## Multiple R-Squared: 0.03504
##
## Test for Bias:
## statistic p-value
## M-estimate 14.972 0.02048
## LS-estimate -6.361 1.00000wtp_tib <- satis_dat %>%
group_by(focus, alcohol) %>%
summarise(n = n(),
mean_wtp = mean(wtp, na.rm = TRUE),
sd_wtp = sd(wtp, na.rm = TRUE),
ci_lo = mean_wtp - 1.96 * (sd_wtp/sqrt(n)),
ci_high = mean_wtp + 1.96 *(sd_wtp/sqrt(n)))
wtp_tib %>%
kable(digits = 2, caption = "Participants' mean willingness to pay in different alcohol and focus conditions.") %>%
kable_styling(full_width = TRUE)| focus | alcohol | n | mean_wtp | sd_wtp | ci_lo | ci_high |
|---|---|---|---|---|---|---|
| control | alcoholic | 30 | 3.13 | 0.90 | 2.81 | 3.46 |
| control | non-alcoholic | 30 | 2.73 | 0.83 | 2.44 | 3.03 |
| hedonic | alcoholic | 28 | 2.89 | 0.88 | 2.57 | 3.22 |
| hedonic | non-alcoholic | 32 | 2.66 | 0.97 | 2.32 | 2.99 |
| utilitarian | alcoholic | 37 | 2.84 | 0.73 | 2.60 | 3.07 |
| utilitarian | non-alcoholic | 30 | 2.70 | 0.88 | 2.39 | 3.01 |
wtp_bar <-ggplot(aes(x=focus,
y = mean_wtp,
fill = alcohol),
data= wtp_tib)+
geom_bar(stat="identity", position=position_dodge())+
geom_errorbar(aes(ymin=ci_lo, ymax=ci_high), width=.2,
position=position_dodge(.9))+
scale_y_continuous(name="willingness to pay (£)",
limits=c(0, 5),
breaks = seq(0,5, 1)) +
scale_x_discrete(name = "focus" )+
scale_fill_manual(values = gender_pal) +
theme_minimal()
wtp_bar
Participants’ willingness to pay for alcoholic and non-alcoholic beer. Error bars indicate confidence intervals (+/- 1.96 SE)
Cognitive Performance
Participants’ cognitive performance was assessed using the inspection time task (10.1016/j.neuroimage.2004.03.047) which measures participants’information processing. Participants completed the task just before, 30 minutes and 60 minutes after consumption.
-pp performance improves as duration of stimulus increases - effect of alcohol, stimulus duration and stage - interactions: alcoholstimulus stimulusstage alcohol*stimulus*stage - no effect of focus
# these are two methods of analysis, should do the same thing, buttakes too long to run
# m_itt <- lmerTest::lmer(correct ~ focus * alcohol *stage *stimulus + (stage*stimulus| subject), data=itt_dat, REML=F)
#
# summary(m_itt)
# contrasts
itt_afx <- afex::aov_4(correct ~ focus*alcohol*stage*stimulus + (stimulus*stage|subject), data = itt_dat)
summary(itt_afx)##
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
##
## Sum Sq num Df Error SS den Df F value Pr(>F)
## (Intercept) 5831.3 1 38.734 178 26797.4015 < 2.2e-16
## focus 0.6 2 38.734 178 1.4063 0.247768
## alcohol 1.8 1 38.734 178 8.1225 0.004888
## focus:alcohol 0.7 2 38.734 178 1.5975 0.205284
## stimulus 34.3 12 15.356 2136 397.8339 < 2.2e-16
## focus:stimulus 0.3 24 15.356 2136 1.4808 0.062289
## alcohol:stimulus 0.2 12 15.356 2136 1.8983 0.030276
## focus:alcohol:stimulus 0.1 24 15.356 2136 0.5317 0.969485
## stage 0.5 2 8.437 356 9.7672 7.421e-05
## focus:stage 0.1 4 8.437 356 1.0657 0.373312
## alcohol:stage 0.0 2 8.437 356 0.2461 0.781995
## focus:alcohol:stage 0.0 4 8.437 356 0.0856 0.986846
## stimulus:stage 0.5 24 18.452 4272 4.8156 9.804e-14
## focus:stimulus:stage 0.2 48 18.452 4272 0.8259 0.798308
## alcohol:stimulus:stage 0.2 24 18.452 4272 1.5525 0.041858
## focus:alcohol:stimulus:stage 0.2 48 18.452 4272 0.7592 0.887985
##
## (Intercept) ***
## focus
## alcohol **
## focus:alcohol
## stimulus ***
## focus:stimulus .
## alcohol:stimulus *
## focus:alcohol:stimulus
## stage ***
## focus:stage
## alcohol:stage
## focus:alcohol:stage
## stimulus:stage ***
## focus:stimulus:stage
## alcohol:stimulus:stage *
## focus:alcohol:stimulus:stage
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Mauchly Tests for Sphericity
##
## Test statistic p-value
## stimulus 0.00230 0.0000e+00
## focus:stimulus 0.00230 0.0000e+00
## alcohol:stimulus 0.00230 0.0000e+00
## focus:alcohol:stimulus 0.00230 0.0000e+00
## stage 0.58653 3.1174e-21
## focus:stage 0.58653 3.1174e-21
## alcohol:stage 0.58653 3.1174e-21
## focus:alcohol:stage 0.58653 3.1174e-21
## stimulus:stage 0.00023 0.0000e+00
## focus:stimulus:stage 0.00023 0.0000e+00
## alcohol:stimulus:stage 0.00023 0.0000e+00
## focus:alcohol:stimulus:stage 0.00023 0.0000e+00
##
##
## Greenhouse-Geisser and Huynh-Feldt Corrections
## for Departure from Sphericity
##
## GG eps Pr(>F[GG])
## stimulus 0.43240 < 2.2e-16 ***
## focus:stimulus 0.43240 0.1380588
## alcohol:stimulus 0.43240 0.0893189 .
## focus:alcohol:stimulus 0.43240 0.8740470
## stage 0.70748 0.0005176 ***
## focus:stage 0.70748 0.3619277
## alcohol:stage 0.70748 0.7031497
## focus:alcohol:stage 0.70748 0.9624416
## stimulus:stage 0.52543 3.705e-08 ***
## focus:stimulus:stage 0.52543 0.7126193
## alcohol:stimulus:stage 0.52543 0.0944288 .
## focus:alcohol:stimulus:stage 0.52543 0.7985395
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## HF eps Pr(>F[HF])
## stimulus 0.4469239 4.817097e-240
## focus:stimulus 0.4469239 1.351004e-01
## alcohol:stimulus 0.4469239 8.677605e-02
## focus:alcohol:stimulus 0.4469239 8.789647e-01
## stage 0.7114920 5.039157e-04
## focus:stage 0.7114920 3.621441e-01
## alcohol:stage 0.7114920 7.044778e-01
## focus:alcohol:stage 0.7114920 9.629908e-01
## stimulus:stage 0.5681699 1.156033e-08
## focus:stimulus:stage 0.5681699 7.226680e-01
## alcohol:stimulus:stage 0.5681699 8.748527e-02
## focus:alcohol:stimulus:stage 0.5681699 8.098047e-01# need to take effects apart and set contrasts
# the main effect of alcohol
alcohol_emm <- emmeans::emmeans(itt_afx, ~alcohol, model = "multivariate")
alcohol_emm # shows us the means## alcohol emmean SE df lower.CL upper.CL
## alcoholic 0.887 0.00777 178 0.872 0.903
## non-alcoholic 0.919 0.00783 178 0.903 0.934
##
## Results are averaged over the levels of: focus, stage, stimulus
## Confidence level used: 0.95# the main effect of stage
stage_emm <- emmeans::emmeans(itt_afx, ~stage, model = "multivariate")
stage_emm## stage emmean SE df lower.CL upper.CL
## stage.1 0.909 0.00632 178 0.896 0.921
## stage.2 0.908 0.00553 178 0.897 0.919
## stage.3 0.892 0.00637 178 0.879 0.904
##
## Results are averaged over the levels of: focus, alcohol, stimulus
## Confidence level used: 0.95emmeans::contrast(stage_emm, method = "trt.vs.ctrl", ref = 1, adjust = "holm")## contrast estimate SE df t.ratio p.value
## stage.2 - stage.1 -0.000635 0.00473 178 -0.134 0.8933
## stage.3 - stage.1 -0.017376 0.00543 178 -3.198 0.0033
##
## Results are averaged over the levels of: focus, alcohol, stimulus
## P value adjustment: holm method for 2 tests#main effect of stimulus
stimulus_emm <- emmeans::emmeans(itt_afx, ~stimulus, model = "multivariate")
stimulus_emm## stimulus emmean SE df lower.CL upper.CL
## stim1 0.701 0.00747 178 0.686 0.716
## stim2 0.846 0.00784 178 0.830 0.861
## stim3 0.857 0.00733 178 0.843 0.871
## stim4 0.862 0.00770 178 0.846 0.877
## stim5 0.921 0.00682 178 0.908 0.934
## stim6 0.924 0.00623 178 0.911 0.936
## stim7 0.934 0.00610 178 0.922 0.946
## stim8 0.944 0.00574 178 0.933 0.955
## stim9 0.945 0.00586 178 0.934 0.957
## stim10 0.948 0.00586 178 0.936 0.959
## stim11 0.947 0.00597 178 0.935 0.959
## stim12 0.954 0.00550 178 0.943 0.965
## stim13 0.956 0.00566 178 0.945 0.967
##
## Results are averaged over the levels of: focus, alcohol, stage
## Confidence level used: 0.95emmeans::contrast(stimulus_emm, method = "poly", adjust = "holm")## contrast estimate SE df t.ratio p.value
## linear 2.76 0.0932 178 29.566 <.0001
## quadratic -5.21 0.1856 178 -28.062 <.0001
## cubic 1.32 0.0952 178 13.873 <.0001
## quartic -7.12 0.9739 178 -7.307 <.0001
## degree 5 2.40 0.2484 178 9.660 <.0001
## degree 6 -3.96 0.3900 178 -10.149 <.0001
##
## Results are averaged over the levels of: focus, alcohol, stage
## P value adjustment: holm method for 6 tests# interaction stimulus:stage
inter_emm <- emmeans::emmeans(itt_afx, c("stage", "stimulus"), model = "multivariate")
inter_emm## stage stimulus emmean SE df lower.CL upper.CL
## stage.1 stim1 0.672 0.01041 178 0.652 0.693
## stage.2 stim1 0.716 0.01077 178 0.695 0.738
## stage.3 stim1 0.715 0.01037 178 0.694 0.735
## stage.1 stim2 0.863 0.00923 178 0.844 0.881
## stage.2 stim2 0.847 0.00921 178 0.829 0.865
## stage.3 stim2 0.828 0.00976 178 0.809 0.847
## stage.1 stim3 0.865 0.00876 178 0.848 0.883
## stage.2 stim3 0.870 0.00889 178 0.852 0.887
## stage.3 stim3 0.836 0.00896 178 0.818 0.854
## stage.1 stim4 0.881 0.00909 178 0.863 0.899
## stage.2 stim4 0.855 0.00907 178 0.837 0.873
## stage.3 stim4 0.849 0.00973 178 0.830 0.868
## stage.1 stim5 0.924 0.00835 178 0.907 0.940
## stage.2 stim5 0.928 0.00749 178 0.914 0.943
## stage.3 stim5 0.911 0.00841 178 0.894 0.927
## stage.1 stim6 0.936 0.00682 178 0.922 0.949
## stage.2 stim6 0.928 0.00727 178 0.914 0.943
## stage.3 stim6 0.907 0.00783 178 0.891 0.922
## stage.1 stim7 0.941 0.00780 178 0.925 0.956
## stage.2 stim7 0.937 0.00638 178 0.924 0.949
## stage.3 stim7 0.926 0.00749 178 0.911 0.940
## stage.1 stim8 0.951 0.00750 178 0.936 0.966
## stage.2 stim8 0.948 0.00575 178 0.937 0.959
## stage.3 stim8 0.932 0.00814 178 0.916 0.948
## stage.1 stim9 0.951 0.00758 178 0.936 0.966
## stage.2 stim9 0.953 0.00625 178 0.941 0.965
## stage.3 stim9 0.932 0.00689 178 0.919 0.946
## stage.1 stim10 0.956 0.00723 178 0.941 0.970
## stage.2 stim10 0.955 0.00653 178 0.942 0.968
## stage.3 stim10 0.932 0.00727 178 0.918 0.947
## stage.1 stim11 0.952 0.00783 178 0.937 0.968
## stage.2 stim11 0.953 0.00610 178 0.941 0.965
## stage.3 stim11 0.936 0.00765 178 0.921 0.952
## stage.1 stim12 0.963 0.00717 178 0.949 0.977
## stage.2 stim12 0.955 0.00566 178 0.944 0.966
## stage.3 stim12 0.943 0.00713 178 0.929 0.957
## stage.1 stim13 0.962 0.00665 178 0.949 0.975
## stage.2 stim13 0.962 0.00626 178 0.950 0.974
## stage.3 stim13 0.944 0.00715 178 0.930 0.958
##
## Results are averaged over the levels of: focus, alcohol
## Confidence level used: 0.95emmeans::contrast(
inter_emm,
c(stage = "trt.vs.ctrl", stimulus = "poly"),
rel = 1,
adjust = "holm"
)## contrast estimate SE df t.ratio p.value
## stage.2 stim1 - stage.1 stim1 0.0439 0.0135 178 3.257 0.0027
## stage.3 stim1 - stage.1 stim1 0.0421 0.0137 178 3.079 0.0027
## stage.1 stim2 - stage.1 stim1 0.1902 0.0106 178 18.003 <.0001
## stage.2 stim2 - stage.1 stim1 0.1742 0.0116 178 15.007 <.0001
## stage.3 stim2 - stage.1 stim1 0.1557 0.0117 178 13.329 <.0001
## stage.1 stim3 - stage.1 stim1 0.1930 0.0101 178 19.207 <.0001
## stage.2 stim3 - stage.1 stim1 0.1973 0.0104 178 18.880 <.0001
## stage.3 stim3 - stage.1 stim1 0.1634 0.0111 178 14.660 <.0001
## stage.1 stim4 - stage.1 stim1 0.2082 0.0110 178 18.840 <.0001
## stage.2 stim4 - stage.1 stim1 0.1829 0.0113 178 16.166 <.0001
## stage.3 stim4 - stage.1 stim1 0.1763 0.0119 178 14.801 <.0001
## stage.1 stim5 - stage.1 stim1 0.2515 0.0110 178 22.820 <.0001
## stage.2 stim5 - stage.1 stim1 0.2560 0.0112 178 22.949 <.0001
## stage.3 stim5 - stage.1 stim1 0.2382 0.0109 178 21.847 <.0001
## stage.1 stim6 - stage.1 stim1 0.2634 0.0106 178 24.888 <.0001
## stage.2 stim6 - stage.1 stim1 0.2560 0.0113 178 22.664 <.0001
## stage.3 stim6 - stage.1 stim1 0.2344 0.0114 178 20.518 <.0001
## stage.1 stim7 - stage.1 stim1 0.2682 0.0107 178 24.987 <.0001
## stage.2 stim7 - stage.1 stim1 0.2641 0.0105 178 25.037 <.0001
## stage.3 stim7 - stage.1 stim1 0.2531 0.0112 178 22.671 <.0001
## stage.1 stim8 - stage.1 stim1 0.2786 0.0108 178 25.780 <.0001
## stage.2 stim8 - stage.1 stim1 0.2756 0.0102 178 26.988 <.0001
## stage.3 stim8 - stage.1 stim1 0.2599 0.0116 178 22.321 <.0001
## stage.1 stim9 - stage.1 stim1 0.2784 0.0110 178 25.341 <.0001
## stage.2 stim9 - stage.1 stim1 0.2806 0.0111 178 25.329 <.0001
## stage.3 stim9 - stage.1 stim1 0.2598 0.0111 178 23.461 <.0001
## stage.1 stim10 - stage.1 stim1 0.2832 0.0110 178 25.756 <.0001
## stage.2 stim10 - stage.1 stim1 0.2827 0.0106 178 26.659 <.0001
## stage.3 stim10 - stage.1 stim1 0.2598 0.0117 178 22.153 <.0001
## stage.1 stim11 - stage.1 stim1 0.2798 0.0114 178 24.602 <.0001
## stage.2 stim11 - stage.1 stim1 0.2806 0.0108 178 25.919 <.0001
## stage.3 stim11 - stage.1 stim1 0.2640 0.0117 178 22.576 <.0001
## stage.1 stim12 - stage.1 stim1 0.2907 0.0109 178 26.560 <.0001
## stage.2 stim12 - stage.1 stim1 0.2828 0.0112 178 25.360 <.0001
## stage.3 stim12 - stage.1 stim1 0.2706 0.0117 178 23.037 <.0001
## stage.1 stim13 - stage.1 stim1 0.2892 0.0108 178 26.842 <.0001
## stage.2 stim13 - stage.1 stim1 0.2896 0.0109 178 26.655 <.0001
## stage.3 stim13 - stage.1 stim1 0.2714 0.0115 178 23.677 <.0001
##
## Results are averaged over the levels of: focus, alcohol
## P value adjustment: holm method for 38 testsitt_dat$stimulus <- as.factor(itt_dat$stimulus) %>%
fct_relevel(.,c("stim1", "stim2", "stim3", "stim4", "stim5", "stim6", "stim7", "stim8", "stim9", "stim10", "stim11", "stim12","stim13"))
itt_plot<-ggplot(data = itt_dat, aes(x = stimulus, y = correct, color = alcohol))+
facet_grid(rows = vars(focus),
cols = vars(stage))+
stat_summary(fun=mean, geom="point") +
scale_y_continuous(name="% correct", limits=c(0.5, 1.0), breaks = seq(0.5,1,0.15)) +
scale_x_discrete(name = "stimulus duration\n(ms)", labels =c("19", "25", "31", "37", "44", "50", "62", "75", "87", "100", "125", "150", "200") )+
scale_color_manual(values = gender_pal) +
theme(axis.text.x = element_text( color="black",
size=2, angle=45))+
theme_minimal()
ggplotly(itt_plot, height = 500, width=1200)Participants’ cognitive performance
Mood
mood_data<- mood_dat %>%
dplyr::select(subject, stage, calm, content, alert)%>%
pivot_longer(cols = c(calm, content, alert),
names_to = "factor",
values_to = "rating")%>%
mutate(condition = subject%/% 1000)%>%
mutate(alcohol = if_else((condition%%2)==1, "alcoholic", "non-alcoholic"))%>%
mutate(focus = case_when(condition == 1 ~ 'control',
condition == 2 ~ 'control',
condition == 3 ~ 'hedonic',
condition == 4 ~ 'hedonic',
condition == 5 ~ 'utilitarian',
condition == 6 ~ 'utilitarian'))
ggplot(data = mood_data, aes(x = stage, y = rating, color = alcohol))+
facet_grid(rows = vars(focus),
cols = vars(factor))+
stat_summary(fun=mean, geom="point") +
scale_y_continuous(name="rating", limits=c(35, 48), breaks = seq(0,50,5)) +
scale_x_discrete(name = "stage", labels = c("1", "2", "3") )+
scale_color_manual(values = gender_pal) +
theme(axis.text.x = element_text( color="black",
size=2, angle=45))+
theme_light()
Participants’mood accross the stages and conditions
mood_data$focus <- as.factor(mood_data$focus)
# afex mixed effects model
mood_afx <- afex::aov_4(rating ~ focus*alcohol*stage*factor + (factor*stage|subject), data = mood_data)
summary(mood_afx)##
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
##
## Sum Sq num Df Error SS den Df F value Pr(>F)
## (Intercept) 2505627 1 183226 179 2447.8317 < 2.2e-16
## focus 1322 2 183226 179 0.6456 0.5255832
## alcohol 1250 1 183226 179 1.2217 0.2705194
## focus:alcohol 9356 2 183226 179 4.5701 0.0115939
## factor 33302 2 72383 358 82.3551 < 2.2e-16
## focus:factor 1339 4 72383 358 1.6552 0.1599128
## alcohol:factor 888 2 72383 358 2.1963 0.1127118
## focus:alcohol:factor 683 4 72383 358 0.8446 0.4975886
## stage 1456 2 39514 358 6.5970 0.0015363
## focus:stage 576 4 39514 358 1.3050 0.2676981
## alcohol:stage 416 2 39514 358 1.8827 0.1536885
## focus:alcohol:stage 402 4 39514 358 0.9107 0.4577106
## factor:stage 8530 4 42998 716 35.5088 < 2.2e-16
## focus:factor:stage 182 8 42998 716 0.3780 0.9324598
## alcohol:factor:stage 1201 4 42998 716 4.9981 0.0005606
## focus:alcohol:factor:stage 477 8 42998 716 0.9932 0.4398359
##
## (Intercept) ***
## focus
## alcohol
## focus:alcohol *
## factor ***
## focus:factor
## alcohol:factor
## focus:alcohol:factor
## stage **
## focus:stage
## alcohol:stage
## focus:alcohol:stage
## factor:stage ***
## focus:factor:stage
## alcohol:factor:stage ***
## focus:alcohol:factor:stage
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Mauchly Tests for Sphericity
##
## Test statistic p-value
## factor 0.78985 0.00000000
## focus:factor 0.78985 0.00000000
## alcohol:factor 0.78985 0.00000000
## focus:alcohol:factor 0.78985 0.00000000
## stage 0.92481 0.00095205
## focus:stage 0.92481 0.00095205
## alcohol:stage 0.92481 0.00095205
## focus:alcohol:stage 0.92481 0.00095205
## factor:stage 0.45122 0.00000000
## focus:factor:stage 0.45122 0.00000000
## alcohol:factor:stage 0.45122 0.00000000
## focus:alcohol:factor:stage 0.45122 0.00000000
##
##
## Greenhouse-Geisser and Huynh-Feldt Corrections
## for Departure from Sphericity
##
## GG eps Pr(>F[GG])
## factor 0.82634 < 2.2e-16 ***
## focus:factor 0.82634 0.171718
## alcohol:factor 0.82634 0.122566
## focus:alcohol:factor 0.82634 0.479646
## stage 0.93007 0.002021 **
## focus:stage 0.93007 0.269465
## alcohol:stage 0.93007 0.156820
## focus:alcohol:stage 0.93007 0.452394
## factor:stage 0.75490 < 2.2e-16 ***
## focus:factor:stage 0.75490 0.894105
## alcohol:factor:stage 0.75490 0.001944 **
## focus:alcohol:factor:stage 0.75490 0.429238
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## HF eps Pr(>F[HF])
## factor 0.8330648 1.429346e-25
## focus:factor 0.8330648 1.712519e-01
## alcohol:factor 0.8330648 1.221801e-01
## focus:alcohol:factor 0.8330648 4.804104e-01
## stage 0.9394397 1.947862e-03
## focus:stage 0.9394397 2.692367e-01
## alcohol:stage 0.9394397 1.564039e-01
## focus:alcohol:stage 0.9394397 4.531321e-01
## factor:stage 0.7693046 2.017256e-21
## focus:factor:stage 0.7693046 8.969476e-01
## alcohol:factor:stage 0.7693046 1.806356e-03
## focus:alcohol:factor:stage 0.7693046 4.299908e-01# main effect stage
stage_emm <- emmeans::emmeans(mood_afx, "stage", model = "multivariate")
stage_emm## stage emmean SE df lower.CL upper.CL
## response1 37.7 0.905 179 35.9 39.4
## response2 39.0 0.802 179 37.5 40.6
## response3 39.9 0.888 179 38.2 41.7
##
## Results are averaged over the levels of: focus, alcohol, factor
## Confidence level used: 0.95emmeans::contrast(
stage_emm,
stage = "trt.vs.ctrl",
ref = 1,
adjust = "holm"
)## contrast estimate SE df t.ratio p.value
## response1 effect -1.222 0.412 179 -2.967 0.0074
## response2 effect 0.165 0.334 179 0.494 0.6216
## response3 effect 1.056 0.344 179 3.073 0.0074
##
## Results are averaged over the levels of: focus, alcohol, factor
## P value adjustment: holm method for 3 tests# main effect mood type = factor
fact_emm <- emmeans::emmeans(mood_afx, "factor", model = "multivariate")
fact_emm## factor emmean SE df lower.CL upper.CL
## calm 34.5 0.868 179 32.8 36.2
## content 37.1 0.995 179 35.1 39.0
## alert 45.0 0.917 179 43.2 46.9
##
## Results are averaged over the levels of: focus, alcohol, stage
## Confidence level used: 0.95emmeans::contrast(
fact_emm,
stage = "pairwise",
adjust = "holm"
)## contrast estimate SE df t.ratio p.value
## calm effect -4.35 0.581 179 -7.484 <.0001
## content effect -1.82 0.379 179 -4.804 <.0001
## alert effect 6.17 0.501 179 12.324 <.0001
##
## Results are averaged over the levels of: focus, alcohol, stage
## P value adjustment: holm method for 3 tests# interaction focus:alcohol
foc_alc_emm <- emmeans::emmeans(mood_afx, c("focus", "alcohol"), model = "multivariate")
foc_alc_emm## focus alcohol emmean SE df lower.CL upper.CL
## control alcoholic 37.2 1.92 179 33.5 41.0
## hedonic alcoholic 43.5 2.02 179 39.6 47.5
## utilitarian alcoholic 38.5 1.80 179 34.9 42.0
## control non-alcoholic 38.0 1.95 179 34.2 41.9
## hedonic non-alcoholic 35.1 1.92 179 31.3 38.9
## utilitarian non-alcoholic 40.9 1.95 179 37.1 44.8
##
## Results are averaged over the levels of: stage, factor
## Confidence level used: 0.95emmeans::contrast(
foc_alc_emm,
stage = "poly",
adjust = "holm"
)## contrast estimate SE df t.ratio p.value
## control alcoholic effect -1.637 1.75 179 -0.935 1.0000
## hedonic alcoholic effect 4.648 1.82 179 2.549 0.0699
## utilitarian alcoholic effect -0.405 1.67 179 -0.243 1.0000
## (control non-alcoholic) effect -0.840 1.77 179 -0.474 1.0000
## (hedonic non-alcoholic) effect -3.795 1.75 179 -2.168 0.1573
## (utilitarian non-alcoholic) effect 2.029 1.77 179 1.144 1.0000
##
## Results are averaged over the levels of: stage, factor
## P value adjustment: holm method for 6 tests#interaction factor:stage
fact_stage_emm <- emmeans::emmeans(mood_afx, c("factor", "stage"), model = "multivariate")
fact_stage_emm## factor stage emmean SE df lower.CL upper.CL
## calm response1 36.7 1.110 179 34.5 38.9
## content response1 36.7 1.150 179 34.4 38.9
## alert response1 39.6 1.054 179 37.5 41.7
## calm response2 33.3 0.996 179 31.3 35.2
## content response2 36.3 1.030 179 34.3 38.4
## alert response2 47.5 0.988 179 45.6 49.5
## calm response3 33.6 1.128 179 31.4 35.8
## content response3 38.2 1.076 179 36.1 40.3
## alert response3 48.0 1.064 179 45.9 50.1
##
## Results are averaged over the levels of: focus, alcohol
## Confidence level used: 0.95emmeans::contrast(
fact_stage_emm,
interaction = c(stage = "pairwise", fact = "trt.vs.ctrl"),
ref = 1,
adjust = "holm"
)## stage_pairwise factor_trt.vs.ctrl estimate SE df t.ratio p.value
## response1 - response2 content - calm -3.151 1.219 179 -2.584 0.0317
## response1 - response3 content - calm -4.664 1.246 179 -3.742 0.0010
## response2 - response3 content - calm -1.513 0.996 179 -1.520 0.2608
## response1 - response2 alert - calm -11.415 1.374 179 -8.311 <.0001
## response1 - response3 alert - calm -11.593 1.472 179 -7.875 <.0001
## response2 - response3 alert - calm -0.178 1.232 179 -0.144 0.8853
##
## Results are averaged over the levels of: focus, alcohol
## P value adjustment: holm method for 6 tests#interaction alcohol:factor:stage
fact_stage_alc_emm <- emmeans::emmeans(mood_afx, c("factor", "alcohol", "stage"), model = "multivariate")
fact_stage_alc_emm## factor alcohol stage emmean SE df lower.CL upper.CL
## calm alcoholic response1 38.7 1.56 179 35.6 41.8
## content alcoholic response1 36.4 1.62 179 33.2 39.6
## alert alcoholic response1 38.7 1.48 179 35.8 41.6
## calm non-alcoholic response1 34.8 1.58 179 31.6 37.9
## content non-alcoholic response1 36.9 1.64 179 33.7 40.1
## alert non-alcoholic response1 40.5 1.50 179 37.5 43.4
## calm alcoholic response2 34.3 1.40 179 31.6 37.1
## content alcoholic response2 35.3 1.45 179 32.5 38.2
## alert alcoholic response2 49.9 1.39 179 47.2 52.7
## calm non-alcoholic response2 32.2 1.42 179 29.4 35.0
## content non-alcoholic response2 37.3 1.47 179 34.4 40.2
## alert non-alcoholic response2 45.1 1.41 179 42.4 47.9
## calm alcoholic response3 35.1 1.59 179 32.0 38.3
## content alcoholic response3 38.9 1.51 179 36.0 41.9
## alert alcoholic response3 50.2 1.50 179 47.3 53.2
## calm non-alcoholic response3 32.0 1.61 179 28.9 35.2
## content non-alcoholic response3 37.4 1.53 179 34.4 40.4
## alert non-alcoholic response3 45.8 1.51 179 42.9 48.8
##
## Results are averaged over the levels of: focus
## Confidence level used: 0.95emmeans::contrast(
fact_stage_alc_emm,
interaction = c(stage = "pairwise", fact = "trt.vs.ctrl"),
ref = 1,
adjust = "holm"
)## stage_pairwise factor_trt.vs.ctrl alcohol_pairwise estimate
## response1 - response2 content - calm alcoholic - (non-alcoholic) -0.346
## response1 - response3 content - calm alcoholic - (non-alcoholic) -2.872
## response2 - response3 content - calm alcoholic - (non-alcoholic) -2.526
## response1 - response2 alert - calm alcoholic - (non-alcoholic) -8.413
## response1 - response3 alert - calm alcoholic - (non-alcoholic) -7.044
## response2 - response3 alert - calm alcoholic - (non-alcoholic) 1.369
## SE df t.ratio p.value
## 2.44 179 -0.142 1.0000
## 2.49 179 -1.152 0.8256
## 1.99 179 -1.268 0.8256
## 2.75 179 -3.063 0.0152
## 2.94 179 -2.392 0.0889
## 2.46 179 0.556 1.0000
##
## Results are averaged over the levels of: focus
## P value adjustment: holm method for 6 tests